{"id":"W4406713676","doi":"10.1016/j.ejrh.2025.102198","title":"Hydrologic model calibration approaches for highly regulated river basin: A comprehensive assessment","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrology Regional Studies","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Calibration; Drainage basin; Hydrological modelling; Structural basin; Environmental science; Hydrology (agriculture); Water resource management; Geography; Remote sensing; Cartography; Geology; Climatology; Statistics; Mathematics; Geomorphology; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003039677,0.0005166883,0.0003014205,0.0006045526,0.0006898085,0.001135261,0.001328299,0.0004720969,0.0005192811],"category_scores_gemma":[0.005062662,0.0002165542,0.000395381,0.001228906,0.0005502987,0.0006605894,0.0004812734,0.000462206,0.00005791869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009018698,"about_ca_system_score_gemma":0.009908411,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7636653,"about_ca_topic_score_gemma":0.8117636,"domain_scores_codex":[0.9991143,0.0002668961,0.00004152462,0.00008955488,0.0003998883,0.00008781444],"domain_scores_gemma":[0.9979997,0.0006507092,0.0002417036,0.0001994759,0.0008150342,0.00009322556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007944289,0.000130826,0.1174022,0.0001848911,0.0002120345,0.0002019908,0.0003198321,0.7984288,0.002347685,0.002937828,0.001424772,0.07632974],"study_design_scores_gemma":[0.0000290449,0.00008134759,0.07381831,0.00008622143,0.00009177856,0.00003710694,0.0003657405,0.9174147,0.002627739,0.000689502,0.004714135,0.00004440818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9618459,0.001546127,0.02573321,0.000733693,0.00001529571,0.0001894182,0.0009130282,0.0005391841,0.008484115],"genre_scores_gemma":[0.9901153,0.0004931896,0.008449131,0.00004288501,0.000003948339,0.00002615757,0.0004492534,0.00003645985,0.0003838676],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7636653,"threshold_uncertainty_score":0.4754531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07420771002542481,"score_gpt":0.2996068985056521,"score_spread":0.2253991884802273,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}